Many organisations dealing with the public are required to manage the length of time people are required to queue for services, for example customers in a bank or passengers going through security checks at an airport. In order to conduct such queue management more effectively, they require statistical estimates of queuing times experienced by people within different time slots throughout the day, so as to optimise their operations and minimise queuing times accordingly.
Currently, techniques employed for monitoring queue lengths and estimating queuing times in airports involve a combination of laser-based passenger counting systems (which cannot track specific individuals) and Bluetooth signal-tracking (which requires passengers to have an active Bluetooth device about their person). Such systems cannot be installed in all areas and are highly inaccurate. Most visual sensor based systems require the ability to accurately track all individuals in a queue in order to derive useful statistics, an unreasonable and unworkable assumption in even modest real-world scenarios where imaging conditions and crowd behaviour is relatively unrestricted.
Therefore, there is required a system and method that overcomes these problems.